# This file contains ShowAttendTell and AllImg model
# ShowAttendTell is from Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
# https://arxiv.org/abs/1502.03044
# AllImg is a model where
# img feature is concatenated with word embedding at every time step as the input of lstm
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import *
# import misc.utils as utils
# import utils as utils
from . import utils
from .CaptionModel import CaptionModel
class OldModel(CaptionModel):
def __init__(self, opt):
super(OldModel, self).__init__()
self.vocab_size = opt.vocab_size
self.input_encoding_size = opt.input_encoding_size
self.rnn_type = opt.rnn_type
self.rnn_size = opt.rnn_size
self.num_layers = opt.num_layers
self.drop_prob_lm = opt.drop_prob_lm
self.seq_length = opt.seq_length
self.fc_feat_size = opt.fc_feat_size
self.att_feat_size = opt.att_feat_size
self.ss_prob = 0.0 # Schedule sampling probability
self.linear = nn.Linear(self.fc_feat_size, self.num_layers * self.rnn_size) # feature to rnn_size
self.embed = nn.Embedding(self.vocab_size + 1, self.input_encoding_size)
self.logit = nn.Linear(self.rnn_size, self.vocab_size + 1)
self.dropout = nn.Dropout(self.drop_prob_lm)
self.init_weights()
def init_weights(self):
initrange = 0.1
self.embed.weight.data.uniform_(-initrange, initrange)
self.logit.bias.data.fill_(0)
self.logit.weight.data.uniform_(-initrange, initrange)
def init_hidden(self, fc_feats):
image_map = self.linear(fc_feats).view(-1, self.num_layers, self.rnn_size).transpose(0, 1)
if self.rnn_type == 'lstm':
return (image_map, image_map)
else:
return image_map
def forward(self, fc_feats, att_feats, seq):
batch_size = fc_feats.size(0)
state = self.init_hidden(fc_feats)
outputs = []
for i in range(seq.size(1) - 1):
if self.training and i >= 1 and self.ss_prob > 0.0: # otherwiste no need to sample
sample_prob = fc_feats.data.new(batch_size).uniform_(0, 1)
sample_mask = sample_prob < self.ss_prob
if sample_mask.sum() == 0:
it = seq[:, i].clone()
else:
sample_ind = sample_mask.nonzero().view(-1)
it = seq[:, i].data.clone()
# prob_prev = torch.exp(outputs[-1].data.index_select(0, sample_ind)) # fetch prev distribution: shape Nx(M+1)
# it.index_copy_(0, sample_ind, torch.multinomial(prob_prev, 1).view(-1))
prob_prev = torch.exp(outputs[-1].data) # fetch prev distribution: shape Nx(M+1)
it.index_copy_(0, sample_ind, torch.multinomial(prob_prev, 1).view(-1).index_select(0, sample_ind))
it = Variable(it, requires_grad=False)
else:
it = seq[:, i].clone()
# break if all the sequences end
if i >= 1 and seq[:, i].data.sum() == 0:
break
xt = self.embed(it)
output, state = self.core(xt, fc_feats, att_feats, state)
output = F.log_softmax(self.logit(self.dropout(output)))
outputs.append(output)
return torch.cat([_.unsqueeze(1) for _ in outputs], 1)
def get_logprobs_state(self, it, tmp_fc_feats, tmp_att_feats, state):
# 'it' is Variable contraining a word index
xt = self.embed(it)
output, state = self.core(xt, tmp_fc_feats, tmp_att_feats, state)
logprobs = F.log_softmax(self.logit(self.dropout(output)))
return logprobs, state
def sample_beam(self, fc_feats, att_feats, opt={}):
beam_size = opt.get('beam_size', 10)
batch_size = fc_feats.size(0)
assert beam_size <= self.vocab_size + 1, 'lets assume this for now, otherwise this corner case causes a few headaches down the road. can be dealt with in future if needed'
seq = torch.LongTensor(self.seq_length, batch_size).zero_()
seqLogprobs = torch.FloatTensor(self.seq_length, batch_size)
# lets process every image independently for now, for simplicity
self.done_beams = [[] for _ in range(batch_size)]
for k in range(batch_size):
tmp_fc_feats = fc_feats[k:k + 1].expand(beam_size, self.fc_feat_size)
tmp_att_feats = att_feats[k:k + 1].expand(*((beam_size,) + att_feats.size()[1:])).contiguous()
state = self.init_hidden(tmp_fc_feats)
beam_seq = torch.LongTensor(self.seq_length, beam_size).zero_()
beam_seq_logprobs = torch.FloatTensor(self.seq_length, beam_size).zero_()
beam_logprobs_sum = torch.zeros(beam_size) # running sum of logprobs for each beam
done_beams = []
for t in range(1):
if t == 0: # input <bos>
it = fc_feats.data.new(beam_size).long().zero_()
xt = self.embed(Variable(it, requires_grad=False))
output, state = self.core(xt, tmp_fc_feats, tmp_att_feats, state)
logprobs = F.log_softmax(self.logit(self.dropout(output)))
self.done_beams[k] = self.beam_search(state, logprobs, tmp_fc_feats, tmp_att_feats, opt=opt)
seq[:, k] = self.done_beams[k][0]['seq'] # the first beam has highest cumulative score
seqLogprobs[:, k] = self.done_beams[k][0]['logps']
# return the samples and their log likelihoods
return seq.transpose(0, 1), seqLogprobs.transpose(0, 1)
def sample(self, fc_feats, att_feats, opt={}):
sample_max = opt.get('sample_max', 1)
beam_size = opt.get('beam_size', 1)
temperature = opt.get('temperature', 1.0)
if beam_size > 1:
return self.sample_beam(fc_feats, att_feats, opt)
batch_size = fc_feats.size(0)
state = self.init_hidden(fc_feats)
seq = []
seqLogprobs = []
for t in range(self.seq_length + 1):
if t == 0: # input <bos>
it = fc_feats.data.new(batch_size).long().zero_()
elif sample_max:
sampleLogprobs, it = torch.max(logprobs.data, 1)
it = it.view(-1).long()
else:
if temperature == 1.0:
prob_prev = torch.exp(logprobs.data).cpu() # fetch prev distribution: shape Nx(M+1)
else:
# scale logprobs by temperature
prob_prev = torch.exp(torch.div(logprobs.data, temperature)).cpu()
it = torch.multinomial(prob_prev, 1).cuda()
sampleLogprobs = logprobs.gather(1, Variable(it,
requires_grad=False)) # gather the logprobs at sampled positions
it = it.view(-1).long() # and flatten indices for downstream processing
xt = self.embed(Variable(it, requires_grad=False))
if t >= 1:
# stop when all finished
if t == 1:
unfinished = it > 0
else:
unfinished = unfinished * (it > 0)
if unfinished.sum() == 0:
break
it = it * unfinished.type_as(it)
seq.append(it) # seq[t] the input of t+2 time step
seqLogprobs.append(sampleLogprobs.view(-1))
output, state = self.core(xt, fc_feats, att_feats, state)
logprobs = F.log_softmax(self.logit(self.dropout(output)), -1)
return torch.cat([_.unsqueeze(1) for _ in seq], 1), torch.cat([_.unsqueeze(1) for _ in seqLogprobs], 1)
class ShowAttendTellCore(nn.Module):
def __init__(self, opt):
super(ShowAttendTellCore, self).__init__()
self.input_encoding_size = opt.input_encoding_size
self.rnn_type = opt.rnn_type
self.rnn_size = opt.rnn_size
self.num_layers = opt.num_layers
self.drop_prob_lm = opt.drop_prob_lm
self.fc_feat_size = opt.fc_feat_size
self.att_feat_size = opt.att_feat_size
self.att_hid_size = opt.att_hid_size
self.rnn = getattr(nn, self.rnn_type.upper())(self.input_encoding_size + self.att_feat_size,
self.rnn_size, self.num_layers, bias=False,
dropout=self.drop_prob_lm)
if self.att_hid_size > 0:
self.ctx2att = nn.Linear(self.att_feat_size, self.att_hid_size)
self.h2att = nn.Linear(self.rnn_size, self.att_hid_size)
self.alpha_net = nn.Linear(self.att_hid_size, 1)
else:
self.ctx2att = nn.Linear(self.att_feat_size, 1)
self.h2att = nn.Linear(self.rnn_size, 1)
def forward(self, xt, fc_feats, att_feats, state):
att_size = att_feats.numel() // att_feats.size(0) // self.att_feat_size
att = att_feats.view(-1, self.att_feat_size)
if self.att_hid_size > 0:
att = self.ctx2att(att) # (batch * att_size) * att_hid_size
att = att.view(-1, att_size, self.att_hid_size) # batch * att_size * att_hid_size
att_h = self.h2att(state[0][-1]) # batch * att_hid_size
att_h = att_h.unsqueeze(1).expand_as(att) # batch * att_size * att_hid_size
dot = att + att_h # batch * att_size * att_hid_size
dot = torch.tanh(dot) # batch * att_size * att_hid_size
dot = dot.view(-1, self.att_hid_size) # (batch * att_size) * att_hid_size
dot = self.alpha_net(dot) # (batch * att_size) * 1
dot = dot.view(-1, att_size) # batch * att_size
else:
att = self.ctx2att(att)(att) # (batch * att_size) * 1
att = att.view(-1, att_size) # batch * att_size
att_h = self.h2att(state[0][-1]) # batch * 1
att_h = att_h.expand_as(att) # batch * att_size
dot = att_h + att # batch * att_size
weight = F.softmax(dot, -1)
att_feats_ = att_feats.view(-1, att_size, self.att_feat_size) # batch * att_size * att_feat_size
att_res = torch.bmm(weight.unsqueeze(1), att_feats_).squeeze(1) # batch * att_feat_size
output, state = self.rnn(torch.cat([xt, att_res], 1).unsqueeze(0), state)
return output.squeeze(0), state
class AllImgCore(nn.Module):
def __init__(self, opt):
super(AllImgCore, self).__init__()
self.input_encoding_size = opt.input_encoding_size
self.rnn_type = opt.rnn_type
self.rnn_size = opt.rnn_size
self.num_layers = opt.num_layers
self.drop_prob_lm = opt.drop_prob_lm
self.fc_feat_size = opt.fc_feat_size
self.rnn = getattr(nn, self.rnn_type.upper())(self.input_encoding_size + self.fc_feat_size,
self.rnn_size, self.num_layers, bias=False,
dropout=self.drop_prob_lm)
def forward(self, xt, fc_feats, att_feats, state):
output, state = self.rnn(torch.cat([xt, fc_feats], 1).unsqueeze(0), state)
return output.squeeze(0), state
class ShowAttendTellModel(OldModel):
def __init__(self, opt):
super(ShowAttendTellModel, self).__init__(opt)
self.core = ShowAttendTellCore(opt)
class AllImgModel(OldModel):
def __init__(self, opt):
super(AllImgModel, self).__init__(opt)
self.core = AllImgCore(opt)